Contents

1 Overview

The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60, and to provide a powerful suite of tools for exploration of NCI-60 data. While CellMiner is an unmatched resource for online exploration of the NCI-60 data, consideration of more specialized scientific questions often requires custom programming. The rcellminer R package complements the functionality of CellMiner, providing programmatic data access, together with functions for data visualization and analysis. These functions are approachable for even beginning R users, as illustrated by the initial examples below. The subsequent case studies, inspired by CellMiner-related publications, show how modest amounts of code can script specialized analyses, integrating multiple types of data to yield new scientific insights. rcellminer functions also provide robust building blocks for more extensive tools, as exemplifed by the package’s interactive Shiny applications.

2 Basics

2.1 Installation

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("rcellminer")
BiocManager::install("rcellminerData")

2.2 Getting Started

Load rcellminer and rcellminerData packages:

library(rcellminer)
library(rcellminerData)

A list of all accessible vignettes and methods is available with the following command.

help.search("rcellminer")

2.3 Searching for Compounds

The NSC number is a numeric identifier for substances submitted to the National Cancer Institute (NCI) for testing and evaluation. It is a registration number for the Developmental Therapeutics Program (DTP) repository, and it is used as the unique identifier for compounds in the CellMiner database. NSC stands for National Service Center.

rcellminer allows users to quickly search for NSC IDs by compound name or partial name. For example, many kinase inhibitors end with the suffix “nib”. Users can quickly search NSCs for compound names with this suffix; queries are case insensitive and are treated as regular expressions.

searchForNscs("nib$")  
##  Fostamatinib     Semaxanib     Gefitinib     Erlotinib     Lapatinib 
##      "365798"      "696819"      "715055"      "718781"      "727989" 
##     Dasatinib     Pazopanib   Selumetinib      Imatinib     Lapatinib 
##      "732517"      "737754"      "741078"      "743414"      "745750" 
##     Nilotinib     Sunitinib      Afatinib     Pazopanib    Crizotinib 
##      "747599"      "750690"      "750691"      "752782"      "756645" 
##  Cabozantinib      Axitinib    Trametinib     Ponatinib     Gefitinib 
##      "757436"      "757441"      "758246"      "758487"      "759856" 
##     Dasatinib    Vandetanib  Cabozantinib   Vemurafenib     Ibrutinib 
##      "759877"      "760766"      "761068"      "761431"      "761910" 
##    Dabrafenib     Bosutinib     Masitinib   Quizartinib     Motesanib 
##      "764134"      "765694"      "755400"      "756647"      "760843" 
##     Tivozanib    Linsitinib   Saracatinib  Lestaurtinib     Alectinib 
##      "758007"      "756652"      "758872"      "772196"      "764040" 
##     Alectinib     Dovitinib     Foretinib    Amuvatinib    Tipifarnib 
##      "764821"      "759661"      "755775"      "754349"      "760444" 
##   Refametinib    Fedratinib     Bosutinib     Cediranib    Lenvatinib 
##      "765866"      "767600"      "755389"      "755606"      "755980" 
##     Intedanib     Neratinib     Sapitinib    Tivantinib    Tandutinib 
##      "756659"      "757439"      "758005"      "758242"      "760841" 
##    brigatinib    Crenolanib      Brivanib    Gandotinib    Varlitinib 
##      "761191"      "763526"      "764481"      "764820"      "764823" 
##   Dacomitinib   Momelotinib  Fostamatinib     Bafetinib    Rebastinib 
##      "765888"      "767598"      "772992"      "773263"      "774831" 
##     Telatinib   Encorafenib    Defactinib   Osimertinib   spebrutinib 
##      "776017"      "778304"      "778364"      "779217"      "780020" 
##     Volitinib    Defactinib    Poziotinib   altiratinib    brigatinib 
##      "782121"      "782549"      "783296"      "784590"      "787457" 
##  gilteritinib     Bafetinib  sitravatinib Acalabrutinib     olmutinib 
##      "787846"      "788186"      "788203"      "791164"      "792848" 
##    ensartinib   ulixertinib    Sulfatinib  zanubrutinib 
##      "793150"      "797771"      "797937"      "799318"

2.4 Profile Visualization

Often, it is useful for researchers to plot multiple data profiles next to each other in order to visually identify patterns. Below are examples for the visualization of various profiles: single drugs and multiple drugs, as well as molecular profiles and combinations of drug and molecular profiles.

# Get Cellminer data
drugAct <- exprs(getAct(rcellminerData::drugData))
molData <- getMolDataMatrices()

# One drug
nsc <- "94600"
plots <- c("drug") 
plotCellMiner(drugAct, molData, plots, nsc, NULL)

# One expression
gene <- "TP53"
plots <- c("exp") 
plotCellMiner(drugAct, molData, plots, NULL, gene)